Model Adaptation · Software component
LoRA Fine-Tuner
Software componentModel AdaptationModelsVariation point (abstract)arc:LoRAFineTuner
A parameter-efficient fine-tuning pipeline that freezes base model weights and trains small low-rank adapter matrices.
Responsibility. Specializes a frozen model by training low-rank adapters.
Also known as: Parameter-efficient fine-tuning (PEFT), Low-Rank Adaptation
Variant of Fine-Tuning Pipeline abstract
When to choose. Choose when GPU memory is constrained and near-full-fine-tuning quality is needed by training only a small adapter.
Variants
| Variant | When to choose |
|---|---|
| QLoRA Fine-Tuner | Choose when even LoRA exceeds available hardware memory and a small quality loss is acceptable. |
Relationships
deployed on structural
trains lifecycle
alternative to variability
Quantitative guidance
As stated by the sources; verify before use.
- PEFT trains ~0.1-1% of weights with performance comparable to full fine-tuning (Ch3.5).
- LoRA cuts fine-tuning memory 60-90%; a 70B model needing ~400GB may need ~40GB, fitting a single high-end GPU (Ch3.5).
- A 70B model can be fine-tuned on one 8x40GB-GPU server instead of a 64-GPU cluster (Ch3.5).
- Activation offloading: 20-30% slower training for 30-50% GPU memory reduction (Ref7.06).
Classification
- Patterns
- LoRAPEFTActivation offloading to host memory
- Technologies
- NVIDIA NeMo Customizer
- Quality attributes
- Cost efficiencyInteraction capability (ISO/IEC 25010)
Sources
- Ch3.5: T. Nguyen, "Prompt Optimization, Few-Shot Learning, Fine-Tuning," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.5. ISBN: 9798244538229.
- Ch10.5: T. Nguyen, "Human-over-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.5. ISBN: 9798244538229.
- Ref7.06: NVIDIA, "Performance Tuning Guide," Megatron Bridge Documentation. Accessed: Sep. 27, 2026. [Online]. Available: https://docs.nvidia.com/nemo/megatron-bridge/latest/performance-guide.html